Mathematical modeling of HIV prevention measures including pre-exposure prophylaxis on HIV incidence in South Korea

Sun Bean Kim, Myoungho Yoon, Nam Su Ku, Min Hyung Kim, Je Eun Song, Jin Young Ahn, Su Jin Jeong, Changsoo Kim, Hee Dae Kwon, Jeehyun Lee, Davey M. Smith, Jun Yong Choi

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20 Citations (Scopus)


Background: Multiple prevention measures have the possibility of impacting HIV incidence in South Korea, including early diagnosis, early treatment, and pre-exposure prophylaxis (PrEP). We investigated how each of these interventions could impact the local HIV epidemic, especially among men who have sex with men (MSM), who have become the major risk group in South Korea. A mathematical model was used to estimate the effects of each these interventions on the HIV epidemic in South Korea over the next 40 years, as compared to the current situation. Methods: We constructed a mathematical model of HIV infection among MSM in South Korea, dividing the MSM population into seven groups, and simulated the effects of early antiretroviral therapy (ART), early diagnosis, PrEP, and combination interventions on the incidence and prevalence of HIV infection, as compared to the current situation that would be expected without any new prevention measures. Results: Overall, the model suggested that the most effective prevention measure would be PrEP. Even though PrEP effectiveness could be lessened by increased unsafe sex behavior, PrEP use was still more beneficial than the current situation. In the model, early diagnosis of HIV infection was also effectively decreased HIV incidence. However, early ART did not show considerable effectiveness. As expected, it would be most effective if all interventions (PrEP, early diagnosis and early treatment) were implemented together. Conclusions: This model suggests that PrEP and early diagnosis could be a very effective way to reduce HIV incidence in South Korea among MSM.

Original languageEnglish
Article numbere90080
JournalPloS one
Issue number3
Publication statusPublished - 2014 Mar 24

All Science Journal Classification (ASJC) codes

  • General


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